TY - CHAP
T1 - Heterogeneous sensor data fusion by deep learning
AU - Liu, Zuozhu
AU - Zhang, Wenyu
AU - Lin, Shaowei
AU - Quek, Tony Q.S.
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Heterogeneous sensor data fusion for decision-making is a challenging field that has gathered significant interest in recent years. In agriculture, for example, environmental conditions such as temperature, illuminance and humidity can be correlated with plant growth data, so that appropriate actions may be taken to maximize crop yield. In this chapter, we will provide an overview of heterogeneous sensor data fusion, including the background, basic deep learning techniques, and how these techniques can be used for sensor data fusion tasks. We will close this chapter with a detailed case study.
AB - Heterogeneous sensor data fusion for decision-making is a challenging field that has gathered significant interest in recent years. In agriculture, for example, environmental conditions such as temperature, illuminance and humidity can be correlated with plant growth data, so that appropriate actions may be taken to maximize crop yield. In this chapter, we will provide an overview of heterogeneous sensor data fusion, including the background, basic deep learning techniques, and how these techniques can be used for sensor data fusion tasks. We will close this chapter with a detailed case study.
UR - http://www.scopus.com/inward/record.url?scp=85118052542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118052542&partnerID=8YFLogxK
U2 - 10.1049/PBCE117E_ch3
DO - 10.1049/PBCE117E_ch3
M3 - Chapter
AN - SCOPUS:85118052542
SP - 57
EP - 77
BT - Data Fusion in Wireless Sensor Networks
PB - Institution of Engineering and Technology
ER -